R Markdown

To run time series on Netflix, Inc. data

Import Data

NetFlix <- read_csv("/Users/bogi/Downloads/netflix_titles.csv") %>% 
  mutate(date_added = mdy(date_added)) %>% clean_names()

glimpse(NetFlix)
## Rows: 7,787
## Columns: 12
## $ show_id      <chr> "s1", "s2", "s3", "s4", "s5", "s6", "s7", "s8", "s9", "s1…
## $ type         <chr> "TV Show", "Movie", "Movie", "Movie", "Movie", "TV Show",…
## $ title        <chr> "3%", "7:19", "23:59", "9", "21", "46", "122", "187", "70…
## $ director     <chr> NA, "Jorge Michel Grau", "Gilbert Chan", "Shane Acker", "…
## $ cast         <chr> "João Miguel, Bianca Comparato, Michel Gomes, Rodolfo Val…
## $ country      <chr> "Brazil", "Mexico", "Singapore", "United States", "United…
## $ date_added   <date> 2020-08-14, 2016-12-23, 2018-12-20, 2017-11-16, 2020-01-…
## $ release_year <dbl> 2020, 2016, 2011, 2009, 2008, 2016, 2019, 1997, 2019, 200…
## $ rating       <chr> "TV-MA", "TV-MA", "R", "PG-13", "PG-13", "TV-MA", "TV-MA"…
## $ duration     <chr> "4 Seasons", "93 min", "78 min", "80 min", "123 min", "1 …
## $ listed_in    <chr> "International TV Shows, TV Dramas, TV Sci-Fi & Fantasy",…
## $ description  <chr> "In a future where the elite inhabit an island paradise f…

Data Screening

Missing data

gg_miss_which(NetFlix)

  • director, cast, country, date_added and rating have missing data!

Missing the variables

gg_miss_upset(NetFlix)

NetFlix %>% head()
## # A tibble: 6 × 12
##   show_id type    title director    cast  country date_added release_year rating
##   <chr>   <chr>   <chr> <chr>       <chr> <chr>   <date>            <dbl> <chr> 
## 1 s1      TV Show 3%    <NA>        João… Brazil  2020-08-14         2020 TV-MA 
## 2 s2      Movie   7:19  Jorge Mich… Demi… Mexico  2016-12-23         2016 TV-MA 
## 3 s3      Movie   23:59 Gilbert Ch… Tedd… Singap… 2018-12-20         2011 R     
## 4 s4      Movie   9     Shane Acker Elij… United… 2017-11-16         2009 PG-13 
## 5 s5      Movie   21    Robert Luk… Jim … United… 2020-01-01         2008 PG-13 
## 6 s6      TV Show 46    Serdar Akar Erda… Turkey  2017-07-01         2016 TV-MA 
## # ℹ 3 more variables: duration <chr>, listed_in <chr>, description <chr>

Comparison movies and TV shows on Netflix

NetFlix %>% count(type, sort = T) %>%
  
  mutate(prop = paste0(round(n / sum(n) * 100, 0), "%")) %>%
  ggplot(aes(x = "", y = prop, fill = type)) +
  geom_bar(
    stat = "identity",
    width = 1,
    color = "steelblue",
    size = 1
  ) +
  coord_polar("y", start = 0) +
  geom_text(
    aes(y = prop, label = prop),
    position = position_stack(vjust = 0.5),
    size = 6,
    col = "white",
    fontface = "bold"
  ) +
  scale_fill_manual (values = c('#e41a1c', '#377eb8')) +
  theme_void() +
  labs(
    title = "Are Movies on Netflix more than TV shows?",
    subtitle = "Pie Plot, proportion of Movies to TV shows",
    caption = "Kaggle: Netflix Movies and TV Shows",
    fill = ""
  )

The result shows Movies are more than TV shows.

Years Difference between release year and added year!

NetFlix <-  NetFlix %>% 
  mutate(year_diff = year(date_added)-release_year) 

NetFlix %>% count(year_diff, sort = F)
## # A tibble: 75 × 2
##    year_diff     n
##        <dbl> <int>
##  1        -3     1
##  2        -2     1
##  3        -1    10
##  4         0  2825
##  5         1  1485
##  6         2   644
##  7         3   439
##  8         4   336
##  9         5   226
## 10         6   218
## # ℹ 65 more rows

Checking example years below

  • 10 items added before release year, 1 year

  • 1 item added before release year, 2 year

  • 1 added before release year, 3 year

datatable(
  NetFlix %>% select(-cast, -description) %>%
    filter(year_diff < 0) %>%
    arrange(year_diff),
  caption = NULL,
  options = list(dom = 't')
)

Years Difference distridution

NetFlix %>% select(year_diff) %>%
  filter(!is.na(year_diff)) %>%
  plot_ly(x = ~ year_diff,
          type = "histogram",
          marker = list(line = list(color = "darkgray",
                                    width = 1))) %>%
  layout(
    title = "Year difference between release_year and date_added",
    yaxis = list(title = "Count",
                 zeroline = FALSE),
    xaxis = list(title = "difference (Years)",
                 zeroline = FALSE)
  )

Checking example of 90 years difference

datatable(NetFlix %>% select(title, type, release_year, date_added, year_diff) %>%
  filter(year_diff > 60) %>% 
    arrange(desc(year_diff)),
  caption = NULL,
  options = list(dom = 't')
)

Rating by Type

NetFlix %>% select(rating, type) %>%
  filter(!is.na(rating)) %>%
  mutate(rating = fct_lump(rating, 5)) %>%
  group_by(rating, type) %>%
  summarise(Count = n()) %>%
  arrange(Count) %>%
  plot_ly(
    x = ~ type ,
    y = ~ Count,
    type = "bar",
    color = ~ rating,
    text = ~ Count,
    textposition = 'outside',
    textfont = list(color = '#000000', size = 12)
  ) %>%
  layout(yaxis = list(categoryorder = "array",
                      categoryarray = ~ Count)) %>%
  layout(
    title = "Rating by Type",
    yaxis = list(title = "Type"),
    xaxis = list(title = "Count"),
    legend = list(title = list(text = '<b> Rating </b>'))
  )

Distribution by Countries Top 10

NetFlix %>% select(country) %>%
  filter(!is.na(country)) %>%
  mutate(country = fct_lump(country, 10)) %>%
  group_by(country) %>%
  summarise(Count = n()) %>%
  arrange(Count) %>%
  plot_ly(
    x = ~ Count ,
    y = ~ country,
    type = "bar",
    orientation = 'h'
  ) %>%
  layout(yaxis = list(categoryorder = "array", categoryarray = ~ Count)) %>%
  layout(
    title = "Items distribution by Country",
    yaxis = list(title = "Country"),
    xaxis = list(title = "Count")
  )
NetFlix %>% select(country) %>%
  filter(!is.na(country)) %>%
  mutate(country = fct_lump(country, 45)) %>%
  group_by(country) %>%
  summarise(Count = n()) %>%
  arrange(Count) %>%
  plot_ly(
    x = ~ Count ,
    y = ~ country,
    type = "bar",
    orientation = 'h'
  ) %>%
  layout(yaxis = list(categoryorder = "array", categoryarray = ~ Count)) %>%
  layout(
    title = "Items distribution by Country",
    yaxis = list(title = "Country"),
    xaxis = list(title = "Count")
  )
    datatable(NetFlix %>% 
                select(-cast, -description) %>% 
      filter(!is.na(country),
             country == "Lebanon"),
      caption = NULL,
      options = list(dom = 't'))

Dataset split to check the durations

movies <- NetFlix %>% select(country, type, duration, rating, title) %>%
  filter(type == "Movie") %>%
  drop_na() %>% 
  mutate(duration_min = parse_number(duration))

tv_show <- NetFlix %>% select(country, type, duration, rating, title) %>%
  filter(type == "TV Show") %>% 
  drop_na() %>% 
  mutate(duration_season = parse_number(duration))

Movies Durations

movies %>%
  plot_ly(
    x = ~ duration_min,
    type = "histogram",
    nbinsx = 40,
    marker = list(
      color = "drakblue",
      line = list(color = "black",
                  width = 1)
    )
  ) %>%
  layout(
    title = "Duration distrbution",
    yaxis = list(title = "Count",
                 zeroline = FALSE),
    xaxis = list(title = "Duration (min)",
                 zeroline = FALSE)
  ) 
datatable(movies %>% select(title, duration_min) %>% 
            filter(duration_min >200) %>% arrange(desc(duration_min)),
      caption = NULL,
      options = list(dom = 't'))

TV-Show Durations

tv_show %>% select(duration_season) %>%
  count(duration_season, sort = TRUE) %>%
  ggplot(aes(
    x = as.factor(duration_season),
    y = n,
    label = n
  )) +
  geom_col(aes(fill = duration_season)) +
  geom_text(vjust = -0.5, size = 3, col = "darkgreen") +
  theme_light() +
  theme(legend.position = "none") +
  labs(x = "Season duration",
       y = "Count",
    title = "Season distrbution",
    subtitle = "Column Plot, Season distrbution",
    caption = "Kaggle: Netflix Movies and TV Shows",
    fill = ""
  )

datatable(tv_show %>% select(title, duration_season) %>% 
            filter(duration_season >15) %>% arrange(desc(duration_season)),
      caption = NULL,
      options = list(dom = 't'))

Time series

ggplotly(
  NetFlix %>% select(date_added) %>%
    filter(!is.na(date_added)) %>%
    mutate(year_added = year(date_added)) %>%
    group_by(year_added) %>%
    summarise(Count = n()) %>%
    arrange(desc(Count)) %>%
    ggplot(aes(
      x = year_added,
      y = Count,
      label = Count
    )) +
    geom_line(size = 1, col = "darkred", alpha = 0.5) +
    geom_col(alpha = 0.6, fill = "steelblue") +
    geom_text(vjust = -0.7, size = 3) +
    theme_light() +
    scale_y_continuous(label = comma) +
    labs(
      x = "Year Added",
      y = "Count",
      title = "Number of Items added per year",
      subtitle = "Column and line Plot, Nunber of Items added per year",
      caption = "Kaggle: Netflix Movies and TV Shows"
    )
)
desc_words_m <- NetFlix %>% select(type, show_id, description) %>%
  filter(type == "Movie") %>% 
    unnest_tokens(word, description) %>%
    anti_join(stop_words)

count_word <- desc_words_m %>%
   count(word, sort = TRUE)


wordcloud(words = count_word$word,  
          freq = count_word$n, 
          min.freq = 50,  
          max.words = nrow(count_word), 
          random.order = FALSE,  
          rot.per = 0.1,  
          colors = brewer.pal(8, "Dark2"))

Most frequent words in description variable For TV-Shows (word cloud)

desc_words_tv <- NetFlix %>% select(type, show_id, description) %>%
  filter(type == "TV Show") %>% 
    unnest_tokens(word, description) %>%
    anti_join(stop_words)

count_word <- desc_words_tv %>%
   count(word, sort = TRUE)


wordcloud(words = count_word$word,  
          freq = count_word$n, 
          min.freq = 30,  
          max.words = nrow(count_word), 
          random.order = FALSE,  
          rot.per = 0.1,  
          colors = brewer.pal(8, "Dark2"))